Want to get started with deep learning?

Prototyping a deep learning image classifier

Thomas Ellebæk, Ferring Pharmaceuticals,

ML02 PhUSE EU Connect 2018,

November 5th 2018

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Pattern detection

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Classification

{Alligator, Beaver, Cat, Dog, …, Zebra}

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Classification

{Alligator, Beaver, Cat, Dog, …, Zebra}

0.01 0.00 0.87 0.07 … 0.00

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Deep learning applicationsTeradata report based on survey conducted July 2017:

”80% report that some form of AI is already in production in their organization”(EB9867_State_of_Artificial_Intelligence_for_the_Enterprises.pdf)

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Deep learning applicationsTeradata report based on survey conducted July 2017:

”80% report that some form of AI is already in production in their organization”(EB9867_State_of_Artificial_Intelligence_for_the_Enterprises.pdf)

www.slideshare.net/AIFrontiers/jeff-dean-trends-and-developments-in-deep-learning-research/8

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Deep learning breakthrough

ImageNet results (top-5)

Err

or

rate

AlexNet

16.4

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Feedforward Neural Network

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Feedforward Neural Network

Shoe size

Hair length

Male

Female

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Feedforward Neural Network

Neuron output:

Activation function:(rectified linear unit – ReLU)

Shoe size and

hair lengthGender

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Feedforward Neural Network

Neuron output:

Activation function:(rectified linear unit – ReLU)

Shoe size and

hair lengthGender

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Feedforward Neural Network

Neuron output:

Activation function:(rectified linear unit – ReLU)

Shoe size and

hair lengthGender

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Feedforward Neural Network

Neuron output:

Activation function:(rectified linear unit – ReLU)

Loss function:(categorical cross entropy)

Shoe size and

hair lengthGender

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Convolutional Neural Network

https://se.mathworks.com/discovery/convolutional-neural-network.html

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Convolution

2D convolution

a b c d

e f g h

i j k l

m n o p

Input

Kernel

… …

… … …

… … …

e

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Convolution

2D convolution 2D cross-correlation

a b c d

e f g h

i j k l

m n o p

Input

Kernel

… …

… … …

… … …

e … …

… … …

… … …

e

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Max pooling

1 4 9 16

2 3 8 15

5 6 7 14

10 11 12 13

4 16

11 14

Input

(2,2) max pooling

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CASE: Cell Counter

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Computing environment

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1 2 3 4 >=5

1 0 0 0 79 0

2 0 0 0 96 0

3 0 0 0 56 0

4 0 0 0 157 0

>=5 0 0 0 52 0

Predicted labels

Act

ual

lab

els

Fitting AlexNet-type model

Validation accuracy: 35.7%

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Final model

3 convolutional layers and

2 dense fully connected

layers

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Final model

3 convolutional layers and

2 dense fully connected

layers

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Error analysis

2 correct predicted examples

5 wrongly predicted examples

Test accuracy: 55.9% >> 35.7%

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Live demo!

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Takeaways and suggested learning

https://machinelearningmastery.com/

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Takeaways and suggested learning

‣ Technology is ready

‣ Data is the most important asset

https://machinelearningmastery.com/

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Takeaways and suggested learning

‣ Technology is ready

‣ Data is the most important asset

https://machinelearningmastery.com/

Questions?